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Course Outline

Current State of the Technology

  • Technologies currently in use
  • Potential future applications

Rules-based AI

  • Simplifying decision processes

Machine Learning

  • Classification
  • Clustering
  • Neural Networks
  • Types of Neural Networks
  • Presentation of practical examples and discussion

Deep Learning

  • Core terminology
  • When to apply Deep Learning and when to avoid it
  • Evaluating computational resources and costs
  • A brief theoretical overview of Deep Neural Networks

Practical Deep Learning (primarily using TensorFlow)

  • Data preparation
  • Selecting the appropriate loss function
  • Choosing the right neural network architecture
  • Balancing accuracy with speed and resource consumption
  • Training neural networks
  • Measuring efficiency and error rates

Sample Applications

  • Anomaly detection
  • Image recognition
  • ADAS (Advanced Driver Assistance Systems)

Requirements

Participants are required to have a background in engineering and experience with programming in any language. However, writing code during the course is not mandatory.

 14 Hours

Number of participants


Price per participant

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